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You have applied the Database per Service pattern. Each service has its own database. Some business transactions, however, span multiple service so you need a mechanism to ensure data consistency across services.
For example, lets imagine that you are building an e-commerce store where customers have a credit limit.
The application must ensure that a new order will not exceed the customer’s credit limit.
Since Orders and Customers are in different databases the application cannot simply use a local ACID transaction.
In theory, it could use a distributed transaction that spans the
Customer Service and the
However, for a variety of reasons distributed transactions are not a viable choice for most modern applications.
Use an event-driven, eventually consistent approach. Each service publishes an event whenever it update it’s data. Other service subscribe to events. When an event is received, a service updates it’s data.
This pattern has the following benefits:
This solution has the following drawbacks:
There are also the following issues to address:
An e-commerce application that uses this approach would work as follows:
Order Servicecreates an Order in a pending state and publishes an
Customer Servicereceives the event and attempts to reserve credit for that Order. It then publishes either a
Credit Reservedevent or a
Order Servicereceives the event from the
Customer Serviceand changes the state of the order to either approved or cancelled
The article Event-Driven Data Management for Microservices by @crichardson describes this pattern